Sensitivity analysis of a passive decay heat removal system under a post-loss of coolant accident condition

Li, Qiao; Homma, Tetsuya

doi:10.1080/00223131.2012.712481

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…and on inherent characteristics (internally stored energy, etc.) [4][5][6][7][8]. However, considering the weak driving forces of passive systems based on natural circulation in the event of a loss of coolant accident (LOCA), particular attention must be given to the passive decay heat removal system since this capability is a strong function of the system geometry and operating conditions [9,10].…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of effective parameters on geyser periodicity in a vertical heated system

Chen

Yang

Liao

et al. 2015

Experimental Thermal and Fluid Science

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Section: Introductionmentioning

confidence: 99%

Experimental investigation of effective parameters on geyser periodicity in a vertical heated system

Chen

Yang

Liao

et al. 2015

Experimental Thermal and Fluid Science

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“…In literature, global methods have been used (a) to conduct “SA” for the input parameters at the level of failure mechanisms with respect to the physical performance outputs 32,44–65 or the component failure probabilities 66,67 and (b) to conduct “IM analysis” for the PRA basic events (i.e. at the level of components) with respect to the system risk.…”

Section: Review Of Existing Sensitivity and Im Analysis Methods In Pramentioning

confidence: 99%

Global importance measure methodology for integrated probabilistic risk assessment

Sakurahara

Reihani

Kee

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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An integrated probabilistic risk assessment framework combines spatio-temporal probabilistic simulations of underlying failure mechanisms with classical probabilistic risk assessment logic consisting of event trees and fault trees. The importance measure methods commonly used in classical probabilistic risk assessment, for example, Fussell–Vesely importance measure and Risk Achievement Worth, generate the ranking of components at the basic event level utilizing the one-at-a-time and local methods. These classical importance measure methods, however, are not sufficient for integrated probabilistic risk assessment where, in addition to the component-level risk importance ranking, the ranking of risk-contributing factors (e.g. physical design parameters) associated with the underlying failure mechanisms is desired. In this research, the global importance measure method is suggested and implemented for the integrated probabilistic risk assessment framework. The global importance measure can account for four key aspects of integrated probabilistic risk assessment, including (a) ranking of input parameters at the failure mechanism level based on the contribution to the system risk metrics, (b) uncertainty of input parameters, (c) non-linearity and interactions among input parameters inside the model, and (d) uncertainty associated with the system risk estimates; therefore, it enhances the accuracy of risk importance ranking when the risk model has a high level of non-linearity, interactions, and uncertainties. This article shows (a) qualitative justifications for the selection of the global importance measure method for the integrated probabilistic risk assessment framework and (b) quantitative proof of concept using three case studies, including two illustrative fault tree examples and one practical application of the integrated probabilistic risk assessment framework for Generic Safety Issue 191 at nuclear power plants (a sump blockage issue following a loss-of-coolant accident).

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“…Reference [1], present most of the global sensitivity analysis methods that are most used in the engineering sciences. Reference [2] use the FAST methods and the analysis of variance method to study the sensitivity of the input parameters of a model that simulates a natural convection decay heat removal system. The authors show that the use of variance analysis is more efficient if the distributions of the output parameters of the model are normal.…”

Section: Introductionmentioning

confidence: 99%

Parametric Sensitivity Analysis of A Compressible Multiphase Flow Model in Porous Medium: Application to the Tsimiroro Madagascar Oil Reservoir

Ahamadi

Rakotondramiarana

Arivelo

2022

EJMATH

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Modeling and numerical methods are two very important fields in physics and engineering sciences. In fluid mechanics, they allow us to study various complex problems and to make predictions of complex phenomena. However, in some cases like the field of petroleum engineering, many parameters like absolute permeability, relative permeability, porosity, capillary pressures, etc. are difficult to measure and / or estimate with certainty. The parametric sensitivity analysis of models provides an overview of the most influential parameters of a model and thus enables the model to be optimized. The study carried out in this work goes in this direction and has made it possible to identify the most influential parameters. The results obtained show that the most influential parameters of the model are the geometric characteristics of the reservoir, porosity and permeability, as well as the injection pressure in the wells.

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Sensitivity analysis of a passive decay heat removal system under a post-loss of coolant accident condition

Cited by 5 publications

References 22 publications

Experimental investigation of effective parameters on geyser periodicity in a vertical heated system

Experimental investigation of effective parameters on geyser periodicity in a vertical heated system

Global importance measure methodology for integrated probabilistic risk assessment

Parametric Sensitivity Analysis of A Compressible Multiphase Flow Model in Porous Medium: Application to the Tsimiroro Madagascar Oil Reservoir

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